Tuesday, August 29, 2017

320 Million Hashes Exposed

Earlier this month (August 2017) Troy Hunt founder of the website Have I been pwned? [0] released over 319 million plaintext passwords [1] compiled from various non-hashed data breaches, in the form of SHA-1 hashes. Making this data public might allow future passwords to be cross-checked in a secure manner in the hopes of preventing password re-use, especially of those from compromised breaches which were in unhashed plaintext.

Our group (in collaboration with@m33x and @tychotithonus) made an attempt to crack/recover as many of the hashes as possible, both for research purposes and of course to satisfy our curiosity while using this opportunity as a challenge. Although each of the pwned password packs released at the time (3 in total at this writing) were labeled as 40-character ASCII-HEX SHA-1 hashes, we worked under the assumption that “No hash list larger than a few hundred thousand entries, contains only one kind of hash!” - and these lists were no exception.

Nested Hashes

Although the majority of the passwords recovered were plaintext, as expected, we also noticed there were a number of plaintexts themselves being hashes or some form of non-plaintext. This suggested that we were dealing with more than just SHA-1.

Out of the roughly 320 million hashes, we were able to recover all but 116 of the SHA-1 hashes, a roughly 99.9999% success rate. In addition, we attempted to take it a step further and resolve as many “nested” hashes (hashes within hashes) as possible to their ultimate plaintext forms.Through the use of MDXfind [2] we were able to identify over 15 different algorithms in use across the pwned-passwords-1.0.txt and the successive update-1 and update-2 packages following that. We also added support for SHA1SHA512x01 to Hashcat [3].

Taking a deeper dive into the found “plaintexts,” we realized there were hashes-within-hashes, hashes of seemingly garbage data, what appears to be “seeded” hashes, and more. Here is a list of the hash types we found:

There are other hashes we have not completely resolved yet - some of which may be seeded hashes. For example, we see:

sha1(md5(md5($salt).md5($pass)))

sha1(md5($salt).md5($pass)))

sha1(md5(md5($salt1).md5($pass)).$salt2)

sha1(md5($salt1).md5($pass).$salt2)

… and much more.

Personal Identifiable Information

We also saw unusual strings from incorrect import/export that was already present in the original leak. This links the hash to the owner of the password, which was clearly not intended by Troy. We found more than 2.5m email addresses and about 230k email:password combinations.

<firstname.lastname@tld><:.,;| /><password>

<truncated-firstname.lastname@tld><:.,;| /><password>

<@tld><:.,;| /><password>

<username><:.,;| /><password>

<firstname.lastname@tld><:.,;| /><some-hash>

Trash / Other Non-Passwords

Furthermore, there were obviously other strings that were not passwords, but rather fragments of files. For example:

006bb7e8893618b02f979dd425e689b4ae64df10:honeyDo you realize who is in this image: http://thecoolpics.com/who.jpg . Just think for a moment and tell me o you realize who is in this image: http://thecoolpics.com/who.jpg . Just think for a moment and tell me soon ;))

Bad Line Parsing

We observed a number of passwords which appeared as they were truncated at length 40 but contained data following the linefeed terminator of the input lists.

n.doe@gmail.com:password:123456jane.doe@

We assumed this was either caused by a parsing error or some anomaly. To recover these strange processed plaintexts, some utilities were coded [4] to emulate the particular behavior of concatenating successive lines while restricting them to 40 characters.

john.doe@gmail.com:password:123456jane.d

ohn.doe@gmail.com:password:123456jane.do

hn.doe@gmail.com:password:123456jane.doe

n.doe@gmail.com:password:123456jane.doe@

Furthermore, to find the correct position where the initial parsing error occurred, we searched our dictionaries from the right to the left (see [4]) concatenating characters like this:

123456jane.doe@ho

o

ho

@ho

e@ho

...

123456jane.doe@ho

An example of a bad/invalid email imported into the haveibeenpwned.com website

Hashcat’s Hexception

During hash processing, we also caught a glimpse into Troy’s methodology. We believe that he processed some “cracked” passwords as well, suggested by the presence of $HEX[] plaintexts. This also revealed a bug in Hashcat’s $HEX[] encoding.

We discovered that Hashcat fails to correctly encode a literal string with $HEX[], if the literal string starts with $HEX[. This means that if you take the output of Hashcat, say from hashcat.pot and try to re-crack it using the passwords in the hashcat.pot file - you will end up with “unsolvable” hashes. As part of our work involves building dictionaries that we can reuse, we consider this a significant bug.

Some tools [5] were put together to properly re-encode the output from Hashcat, into the proper string:

$HEX[244845585b623436653635373737393666373236625d]

This then works properly as a reusable password with Hashcat and MDXfind, as it decodes into the literal string:

$HEX[b46e6577796f726b]

This issue has been resolved in a beta version of Hashcat [6].

We also uncovered a second bug in Hashcat, which was later corrected in a beta version. When using certain rules, we found that the solutions that Hashcat was offering also did not hash back to the correct value. We ended up with hundreds of “solutions” that really were not solutions at all. This is one of the reasons that we always try to double-check our work, to ensure that we have accurate hashes and plaintexts.

As a final check, we took just the SHA1x01 passwords we found and re-ran them through both Hashcat (Beta v3.6.0-351-gec874c1) and MDXfind. The results were quite illuminating. The test system used was a 4 core Intel Core i7-6700K system, with 4x GTX1080 cards and 64GB of memory. Using Hashcat, we found that loading more than about 250,000,000 hashes at a time was not possible [7] and as a result, the list was broken up into chunks of 225m hashes.

Program

Time to Complete

Hashes Found

Hashcat

55 minutes

318,932,512

MDXfind (all hashes)

9 minutes

318,933,582

MDXfind (225m chunks)

9 minutes

318,933,582

From our usage patterns, it is evident that both applications have their strengths and caveats. MDXfind shows its strength when the hashlist is too large to fit into GPU memory, when many algorithms need to be checked in parallel and when very long password strings need to be tested. Hashcat, on the other hand, shines when parallel compute is needed; such as running large rule sets and large keyspaces. Using the tools in tandem gives us the best of both worlds since we can feed the left list of each successive attack into either program to achieve optimal efficiency and coverage.

To further illustrate the problem with password reuse (and the importance of validation), the hashes were re-run using just the found password of Hashcat (Beta v3.6.0-351-gec874c1). This resulted in 86,954 hashes not being recovered. These are primarily due to the $HEX encoding error that Hashcat makes.

Distributed Tasks

Once the hashlist was small enough where the size of the hashlist had negligible effects on search speed, distributed brute-force and mask attacks were conducted via Hashtopussy [8] a Hashcat wrapper. Combining our hardware, we were able to achieve peak speeds of over 180GH/s on SHA-1, to put things into perspective that's roughly the speed of 25x GTX1080s. We were able to cover ?a length 1-8, ?l?d length 9-10 and ?b length 1-6 effortlessly.

Statistical Properties

In order to speed up the analysis of such a large volume of plaintexts, a custom tool was coded “Panal” (will be released at a later time) to quickly and accurately analyse our large dataset of over 320 million passwords. The longest password we found was 400 characters, while the shortest was only 3 characters long. About 0.06% of passwords were 50 characters or longer with 96.67% of passwords being 16 characters or less. Roughly 87.3% of passwords fall into the character set of LowerNum 47.5%, LowerCase 24.75%, Num 8.15%, and MixedNum 6.89% respectively. In addition we saw UTF-8 encoded passwords along with passes containing control characters. See [9] for full Panal output.

Summary

Blocking common passwords during account creation has positive effects on the overall password security of a website [10]. While blacklisting 320m leaked passwords might sound like a good idea to further improve password security, it can have unforeseeable consequences on usability (i.e, the level of user frustration). Conventional blacklist approaches typically include the 10k most common passwords to limit online password guessing attack consequences. Until now, there has been no evidence to support which blacklist size provides an optimal balance.

In the statistical section could you PLEASE define your terms. While I/we can guess, it would be nice if at least you provided a link to the definitions character set terms used.

PLUS, you used 2 different sets of abbreviations and VALUES. One set in the text and a similar but different set in the chart ie LowerCase 24.75 in text and Lcase 26% on the chart. I assume/guess they are referring to the same thing ...

I'd like to see some review on how passwords are built using Automata theory. That is how are most passwords selected using types of chars. Such as, x% of passwords start with a capital letter, followed by 4 undercase, followed by a number and special character. This information could be used to better configure hashcat's brute force options to better crack most passwords.